title: Dictionary learning improves subtyping of breast cancer aCGH data.
abstract: The advent of Comparative Genomic Hybridization (CGH) data led to the development of new mathematical models and computational methods to automatically infer chromosomal alterations. In this work we tackle a standard clustering problem exploiting the good representation properties of a novel method based on dictionary learning. The identified dictionary atoms, which show co-occuring shared alterations among samples, can be easily interpreted by domain experts. We compare a state-of-the-art approach with an original method on a breast cancer dataset.
authors.forename: Salvatore
authors.initials: S
authors.lastname: Masecchia
authors.forename: Annalisa
authors.initials: A
authors.lastname: Barla
authors.forename: Saverio
authors.initials: S
authors.lastname: Salzo
authors.forename: Alessandro
authors.initials: A
authors.lastname: Verri
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000235:genetics*; D002869:Chromosome Aberrations; D002886:Chromosomes, Human, Pair 17 / Q000235:genetics; D002898:Chromosomes, Human, Pair 8 / Q000235:genetics; D016000:Cluster Analysis; D055028:Comparative Genomic Hybridization / Q000379:methods*; D005260:Female; D055106:Genome-Wide Association Study; D006801:Humans; D008962:Models, Theoretical